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Journal of Arid Land  2023, Vol. 15 Issue (12): 1439-1454    DOI: 10.1007/s40333-023-0035-2
Research article     
Analyzing environmental flow supply in the semi-arid area through integrating drought analysis and optimal operation of reservoir
College of Science and Engineering, James Cook University, Townsville 4811, Australia
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This study proposes a novel form of environmental reservoir operation through integrating environmental flow supply, drought analysis, and evolutionary optimization. This study demonstrates that simultaneous supply of downstream environmental flow of reservoir as well as water demand is challenging in the semi-arid area especially in dry years. In this study, water supply and environmental flow supply were 40% and 30% in the droughts, respectively. Moreover, mean errors of supplying water demand as well as environmental flow in dry years were 6 and 9 m3/s, respectively. Hence, these results highlight that ecological stresses of the downstream aquatic habitats as well as water supply loss are considerably escalated in dry years, which implies even using environmental optimal operation is not able to protect downstream aquatic habitats properly in the severe droughts. Moreover, available storage in reservoir will be remarkably reduced (averagely more than 30×106 m3 compared with optimal storage equal to 70×106 m3), which implies strategic storage of reservoir might be threatened. Among used evolutionary algorithms, particle swarm optimization (PSO) was selected as the best algorithm for solving the novel proposed objective function. The significance of this study is to propose a novel objective function to optimize reservoir operation in which environmental flow supply is directly addressed and integrated with drought analysis. This novel form of optimization system can overcome uncertainties of the conventional objective function due to considering environmental flow in the objective function as well as drought analysis in the context of reservoir operation especially applicable in semi-arid areas. The results indicate that using either other water resources for water supply or reducing water demand is the only solution for managing downstream ecological impacts of the river ecosystem. In other words, the results highlighted that replanning of water resources in the study area is necessary. Replacing the conventional optimization system for reservoir operation in the semi-arid area with proposed optimization system is recommendable to minimize the negotiations between stakeholders and environmental managers.

Key wordsoptimization      reservoir operation      droughts      metaheuristic algorithms      environmental flow regime     
Received: 01 August 2023      Published: 31 December 2023
Corresponding Authors: *Mahdi SEDIGHKIA (E-mail:
Cite this article:

Mahdi SEDIGHKIA, Bithin DATTA. Analyzing environmental flow supply in the semi-arid area through integrating drought analysis and optimal operation of reservoir. Journal of Arid Land, 2023, 15(12): 1439-1454.

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Fig. 1 Workflow of this study. SDI, stream drought index.
Fig. 2 Location of the Latian Dam and upstream of the Jajrood River basin
Data Description
Hydrological data Historical recorded river flows (inflow) to reservoir for a long-term period (55 a) were available in the data bank of regional water authority
Evaporation data Average monthly evaporation data from the surface of reservoir was available based on long-term data recorded in the regional weather station near to reservoir
Environmental flow time series A recent regional technical report provided environmental flow analysis data (Abdoli and Sedighkia, 2019)
Water demand time series Available in the regional water authority dataset
Table 1 Introducing datasets
State Description Criterion of SDI
0 Non-drought SDI≥0.0
1 Mild drought -1.0≤SDI<0.0
2 Moderate drought -1.5≤SDI< -1.0
3 Severe drought -2.0≤SDI< -1.5
4 Extreme drought SDI< -2.0
Table 2 Criteria for definition of SDI
Fig. 3 Flowchart of particle swarm optimization (PSO)
Fig. 4 Flowchart of differential evolution (DE) algorithm
Fig. 5 Flowchart of biogeography-based optimization (BBO)
Fig. 6 Result of stream drought index (SDI)
Fig. 7 Time series of water demand, ideal environmental flow, the minimum environmental flow, and reservoir inflow
Fig. 8 Optimal release for water demand by different evolutionary algorithms. PSO, particle swarm optimization; BBO, biogeography-based optimization; DE, differential evolution algorithm. The abbreviations are the same in the following figures.
Fig. 9 Optimal release for environment by different evolutionary algorithms
displays the optimal storage in reservoir. Results demonstrate that performance of algorithms is different in term of storage in reservoir. Some points must be noted in optimal storage of reservoir. First, droughts might escalate the challenges of storage management. In fact, optimal storage is not accessible during dry years or reducing storage benefits is inevitable. However, results demonstrate that performance of the minimum storage penalty function was perfect. Storage is not less than the minimum operational storage in all time steps.
Fig. 10 Optimal storage in reservoir
Reliability index of water supply (%) 38.80 38.60 39.40
Reliability index of environmental flow (%) 29.94 31.20 29.10
RMSE of water supply (m3/s) 6.80 6.80 6.90
RMSE of environmental flow (m3/s) 9.20 9.20 8.95
RMSE of storage (×106 m3) 34.60 37.30 36.70
Table 3 Measurement indices of reservoir operation optimization
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